{
 "cells": [
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# 超参数配置和数据准备",
   "id": "ee80d60ffa12b639"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-23T07:19:31.537749Z",
     "start_time": "2025-02-23T07:19:31.526469Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from utils.data_preprocessing import *\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import torch\n",
    "\n",
    "mpl.rcParams['font.sans-serif'] = ['SimHei']\n",
    "mpl.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "subj = 0\n",
    "task = 0\n",
    "subsample = 5\n",
    "project_fname = './'\n",
    "paper_pic_dir = 'C:\\\\Users\\\\86136\\\\Desktop\\\\毕业设计\\\\picture'\n",
    "pic_dir = os.path.join('./model_pic/DDPM/', 'suj' + str(subj), 'task' + str(task))\n",
    "output_dir = './model_checkpoints/ckpt-DDPM/'\n",
    "output_dir = os.path.join(output_dir, 'suj' + str(subj), 'task' + str(task))\n",
    "\n",
    "print(\"图片输出文件夹：\", pic_dir)\n",
    "print(\"模型输出文件夹：\", output_dir)\n",
    "\n",
    "os.makedirs(pic_dir, exist_ok=True)\n",
    "os.makedirs(output_dir, exist_ok=True)\n",
    "    \n",
    "channel_inds = (0, 7, 9, 11, 19)"
   ],
   "id": "d9da47bb63411189",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "图片输出文件夹： ./model_pic/DDPM/suj0/task0\n",
      "模型输出文件夹： ./model_checkpoints/ckpt-DDPM/suj0/task0\n"
     ]
    }
   ],
   "execution_count": 27
  },
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-02-23T06:58:07.905449Z",
     "start_time": "2025-02-23T06:58:00.313496Z"
    }
   },
   "source": [
    "X_train_cwt_norm = WGAN_preprcessing_method(project_fname, subsample, subj, task, channel_inds)\n",
    "print(X_train_cwt_norm.shape)\n",
    "\n",
    "# 将维度转换为[trial, channel, 频段, time]\n",
    "X_train_cwt_norm = X_train_cwt_norm.transpose(0, 3, 1, 2)\n",
    "# 将第三个维度\n",
    "print(\"转换后的维度：\", X_train_cwt_norm.shape)\n",
    "# 画出一张图\n",
    "show_trial = 0\n",
    "show_channel = 0\n",
    "plt.figure()\n",
    "plt.imshow(X_train_cwt_norm[show_trial, show_channel, :, :], aspect='auto')\n",
    "plt.colorbar()\n",
    "plt.title(\"归一化CWT真实信号 试次{} 通道{}\".format(show_trial, show_channel))\n",
    "plt.show()\n",
    "\n",
    "# 转化为tensor\n",
    "X_train_cwt_norm = torch.tensor(X_train_cwt_norm, dtype=torch.float32)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/tmp/pycharm_project_425\n",
      "Shapes: x = (300, 22, 200, 1), y = (300,), person = (300,)\n",
      "正在进行离散 CWT 卷积...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "试次: 100%|██████████| 300/300 [00:05<00:00, 59.49it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_cwt_norm = (300, 50, 200, 5)\n",
      "(300, 50, 200, 5)\n",
      "转换后的维度： (300, 5, 50, 200)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 2
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "# 创建训练配置\n",
    "为方便起见，创建一个 TrainingConfig 类，其中包含训练超参数"
   ],
   "id": "8f0da673910c9b84"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-23T06:58:15.618993Z",
     "start_time": "2025-02-23T06:58:15.608733Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from dataclasses import dataclass\n",
    "\n",
    "@dataclass\n",
    "class TrainingConfig:\n",
    "    # 图像尺寸\n",
    "    image_size = (64, 256)\n",
    "    # 训练批次大小\n",
    "    train_batch_size = 36\n",
    "    # 评估批次大小 \n",
    "    eval_batch_size = 36\n",
    "    # 训练轮数\n",
    "    num_epochs = 60\n",
    "    # 梯度累积步数（累计几次梯度更新一次参数）\n",
    "    gradient_accumulation_steps = 1\n",
    "    # 学习率\n",
    "    learning_rate = 1e-4\n",
    "    # 学习率衰减\n",
    "    lr_warmup_steps = 500\n",
    "    \n",
    "    save_image_epochs = 10\n",
    "    save_model_epochs = 20\n",
    "    \n",
    "    mixed_precision = \"no\"  # `no` for float32, `fp16` for automatic mixed precision\n",
    "    output_dir = output_dir\n",
    "    # 是否上传模型到HF Hub\n",
    "    push_to_hub = False  # whether to upload the saved model to the HF Hub\n",
    "    hub_model_id = \"hibiscus/test_model\"  # the name of the repository to create on the HF Hub\n",
    "    hub_private_repo = None\n",
    "    overwrite_output_dir = True  # overwrite the old model when re-running the notebook\n",
    "    seed = 42\n",
    "    \n",
    "config = TrainingConfig()"
   ],
   "id": "dc385a3921b4c322",
   "outputs": [],
   "execution_count": 3
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# 图像预处理",
   "id": "c3815635f7bf873c"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-23T06:58:19.896735Z",
     "start_time": "2025-02-23T06:58:19.141863Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 预处理保证图像尺寸正确\n",
    "from torchvision import transforms\n",
    "\n",
    "preprocess = transforms.Compose([\n",
    "    transforms.Resize(config.image_size),\n",
    "])\n",
    "\n",
    "def transform_image(img):\n",
    "    return preprocess(img)\n",
    "\n",
    "X_train_cwt_norm = transform_image(X_train_cwt_norm)\n",
    "print(\"转换后的维度：\", X_train_cwt_norm.shape)"
   ],
   "id": "5d5ea367356adf79",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "转换后的维度： torch.Size([300, 5, 64, 256])\n"
     ]
    }
   ],
   "execution_count": 4
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# 创建一个 UNet2DModel",
   "id": "d85466f81d403159"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-23T06:58:22.997028Z",
     "start_time": "2025-02-23T06:58:21.422254Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from diffusers import UNet2DModel\n",
    "\n",
    "model = UNet2DModel(\n",
    "    sample_size=config.image_size,  # the target image resolution\n",
    "    in_channels=5,  # the number of input channels, 3 for RGB images\n",
    "    out_channels=5,  # the number of output channels\n",
    "    layers_per_block=2,  # how many ResNet layers to use per UNet block\n",
    "    block_out_channels=(128, 128, 256, 256, 512, 512),  # the number of output channels for each UNet block\n",
    "    down_block_types=(\n",
    "        \"DownBlock2D\",  # a regular ResNet downsampling block\n",
    "        \"DownBlock2D\",\n",
    "        \"DownBlock2D\",\n",
    "        \"DownBlock2D\",\n",
    "        \"AttnDownBlock2D\",  # a ResNet downsampling block with spatial self-attention\n",
    "        \"DownBlock2D\",\n",
    "    ),\n",
    "    up_block_types=(\n",
    "        \"UpBlock2D\",  # a regular ResNet upsampling block\n",
    "        \"AttnUpBlock2D\",  # a ResNet upsampling block with spatial self-attention\n",
    "        \"UpBlock2D\",\n",
    "        \"UpBlock2D\",\n",
    "        \"UpBlock2D\",\n",
    "        \"UpBlock2D\",\n",
    "    ),\n",
    ")\n",
    "print(model)\n"
   ],
   "id": "a2f6ad62d7b3f2",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "UNet2DModel(\n",
      "  (conv_in): Conv2d(5, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "  (time_proj): Timesteps()\n",
      "  (time_embedding): TimestepEmbedding(\n",
      "    (linear_1): Linear(in_features=128, out_features=512, bias=True)\n",
      "    (act): SiLU()\n",
      "    (linear_2): Linear(in_features=512, out_features=512, bias=True)\n",
      "  )\n",
      "  (down_blocks): ModuleList(\n",
      "    (0-1): 2 x DownBlock2D(\n",
      "      (resnets): ModuleList(\n",
      "        (0-1): 2 x ResnetBlock2D(\n",
      "          (norm1): GroupNorm(32, 128, eps=1e-05, affine=True)\n",
      "          (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "          (time_emb_proj): Linear(in_features=512, out_features=128, bias=True)\n",
      "          (norm2): GroupNorm(32, 128, eps=1e-05, affine=True)\n",
      "          (dropout): Dropout(p=0.0, inplace=False)\n",
      "          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "          (nonlinearity): SiLU()\n",
      "        )\n",
      "      )\n",
      "      (downsamplers): ModuleList(\n",
      "        (0): Downsample2D(\n",
      "          (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (2): DownBlock2D(\n",
      "      (resnets): ModuleList(\n",
      "        (0): ResnetBlock2D(\n",
      "          (norm1): GroupNorm(32, 128, eps=1e-05, affine=True)\n",
      "          (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "          (time_emb_proj): Linear(in_features=512, out_features=256, bias=True)\n",
      "          (norm2): GroupNorm(32, 256, eps=1e-05, affine=True)\n",
      "          (dropout): Dropout(p=0.0, inplace=False)\n",
      "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "          (nonlinearity): SiLU()\n",
      "          (conv_shortcut): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1))\n",
      "        )\n",
      "        (1): ResnetBlock2D(\n",
      "          (norm1): GroupNorm(32, 256, eps=1e-05, affine=True)\n",
      "          (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "          (time_emb_proj): Linear(in_features=512, out_features=256, bias=True)\n",
      "          (norm2): GroupNorm(32, 256, eps=1e-05, affine=True)\n",
      "          (dropout): Dropout(p=0.0, inplace=False)\n",
      "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "          (nonlinearity): SiLU()\n",
      "        )\n",
      "      )\n",
      "      (downsamplers): ModuleList(\n",
      "        (0): Downsample2D(\n",
      "          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (3): DownBlock2D(\n",
      "      (resnets): ModuleList(\n",
      "        (0-1): 2 x ResnetBlock2D(\n",
      "          (norm1): GroupNorm(32, 256, eps=1e-05, affine=True)\n",
      "          (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "          (time_emb_proj): Linear(in_features=512, out_features=256, bias=True)\n",
      "          (norm2): GroupNorm(32, 256, eps=1e-05, affine=True)\n",
      "          (dropout): Dropout(p=0.0, inplace=False)\n",
      "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "          (nonlinearity): SiLU()\n",
      "        )\n",
      "      )\n",
      "      (downsamplers): ModuleList(\n",
      "        (0): Downsample2D(\n",
      "          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (4): AttnDownBlock2D(\n",
      "      (attentions): ModuleList(\n",
      "        (0-1): 2 x Attention(\n",
      "          (group_norm): GroupNorm(32, 512, eps=1e-05, affine=True)\n",
      "          (to_q): Linear(in_features=512, out_features=512, bias=True)\n",
      "          (to_k): Linear(in_features=512, out_features=512, bias=True)\n",
      "          (to_v): Linear(in_features=512, out_features=512, bias=True)\n",
      "          (to_out): ModuleList(\n",
      "            (0): Linear(in_features=512, out_features=512, bias=True)\n",
      "            (1): Dropout(p=0.0, inplace=False)\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "      (resnets): ModuleList(\n",
      "        (0): ResnetBlock2D(\n",
      "          (norm1): GroupNorm(32, 256, eps=1e-05, affine=True)\n",
      "          (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "          (time_emb_proj): Linear(in_features=512, out_features=512, bias=True)\n",
      "          (norm2): GroupNorm(32, 512, eps=1e-05, affine=True)\n",
      "          (dropout): Dropout(p=0.0, inplace=False)\n",
      "          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "          (nonlinearity): SiLU()\n",
      "          (conv_shortcut): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1))\n",
      "        )\n",
      "        (1): ResnetBlock2D(\n",
      "          (norm1): GroupNorm(32, 512, eps=1e-05, affine=True)\n",
      "          (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "          (time_emb_proj): Linear(in_features=512, out_features=512, bias=True)\n",
      "          (norm2): GroupNorm(32, 512, eps=1e-05, affine=True)\n",
      "          (dropout): Dropout(p=0.0, inplace=False)\n",
      "          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "          (nonlinearity): SiLU()\n",
      "        )\n",
      "      )\n",
      "      (downsamplers): ModuleList(\n",
      "        (0): Downsample2D(\n",
      "          (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (5): DownBlock2D(\n",
      "      (resnets): ModuleList(\n",
      "        (0-1): 2 x ResnetBlock2D(\n",
      "          (norm1): GroupNorm(32, 512, eps=1e-05, affine=True)\n",
      "          (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "          (time_emb_proj): Linear(in_features=512, out_features=512, bias=True)\n",
      "          (norm2): GroupNorm(32, 512, eps=1e-05, affine=True)\n",
      "          (dropout): Dropout(p=0.0, inplace=False)\n",
      "          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "          (nonlinearity): SiLU()\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (up_blocks): ModuleList(\n",
      "    (0): UpBlock2D(\n",
      "      (resnets): ModuleList(\n",
      "        (0-2): 3 x ResnetBlock2D(\n",
      "          (norm1): GroupNorm(32, 1024, eps=1e-05, affine=True)\n",
      "          (conv1): Conv2d(1024, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "          (time_emb_proj): Linear(in_features=512, out_features=512, bias=True)\n",
      "          (norm2): GroupNorm(32, 512, eps=1e-05, affine=True)\n",
      "          (dropout): Dropout(p=0.0, inplace=False)\n",
      "          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "          (nonlinearity): SiLU()\n",
      "          (conv_shortcut): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1))\n",
      "        )\n",
      "      )\n",
      "      (upsamplers): ModuleList(\n",
      "        (0): Upsample2D(\n",
      "          (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (1): AttnUpBlock2D(\n",
      "      (attentions): ModuleList(\n",
      "        (0-2): 3 x Attention(\n",
      "          (group_norm): GroupNorm(32, 512, eps=1e-05, affine=True)\n",
      "          (to_q): Linear(in_features=512, out_features=512, bias=True)\n",
      "          (to_k): Linear(in_features=512, out_features=512, bias=True)\n",
      "          (to_v): Linear(in_features=512, out_features=512, bias=True)\n",
      "          (to_out): ModuleList(\n",
      "            (0): Linear(in_features=512, out_features=512, bias=True)\n",
      "            (1): Dropout(p=0.0, inplace=False)\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "      (resnets): ModuleList(\n",
      "        (0-1): 2 x ResnetBlock2D(\n",
      "          (norm1): GroupNorm(32, 1024, eps=1e-05, affine=True)\n",
      "          (conv1): Conv2d(1024, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "          (time_emb_proj): Linear(in_features=512, out_features=512, bias=True)\n",
      "          (norm2): GroupNorm(32, 512, eps=1e-05, affine=True)\n",
      "          (dropout): Dropout(p=0.0, inplace=False)\n",
      "          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "          (nonlinearity): SiLU()\n",
      "          (conv_shortcut): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1))\n",
      "        )\n",
      "        (2): ResnetBlock2D(\n",
      "          (norm1): GroupNorm(32, 768, eps=1e-05, affine=True)\n",
      "          (conv1): Conv2d(768, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "          (time_emb_proj): Linear(in_features=512, out_features=512, bias=True)\n",
      "          (norm2): GroupNorm(32, 512, eps=1e-05, affine=True)\n",
      "          (dropout): Dropout(p=0.0, inplace=False)\n",
      "          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "          (nonlinearity): SiLU()\n",
      "          (conv_shortcut): Conv2d(768, 512, kernel_size=(1, 1), stride=(1, 1))\n",
      "        )\n",
      "      )\n",
      "      (upsamplers): ModuleList(\n",
      "        (0): Upsample2D(\n",
      "          (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (2): UpBlock2D(\n",
      "      (resnets): ModuleList(\n",
      "        (0): ResnetBlock2D(\n",
      "          (norm1): GroupNorm(32, 768, eps=1e-05, affine=True)\n",
      "          (conv1): Conv2d(768, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "          (time_emb_proj): Linear(in_features=512, out_features=256, bias=True)\n",
      "          (norm2): GroupNorm(32, 256, eps=1e-05, affine=True)\n",
      "          (dropout): Dropout(p=0.0, inplace=False)\n",
      "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "          (nonlinearity): SiLU()\n",
      "          (conv_shortcut): Conv2d(768, 256, kernel_size=(1, 1), stride=(1, 1))\n",
      "        )\n",
      "        (1-2): 2 x ResnetBlock2D(\n",
      "          (norm1): GroupNorm(32, 512, eps=1e-05, affine=True)\n",
      "          (conv1): Conv2d(512, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "          (time_emb_proj): Linear(in_features=512, out_features=256, bias=True)\n",
      "          (norm2): GroupNorm(32, 256, eps=1e-05, affine=True)\n",
      "          (dropout): Dropout(p=0.0, inplace=False)\n",
      "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "          (nonlinearity): SiLU()\n",
      "          (conv_shortcut): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))\n",
      "        )\n",
      "      )\n",
      "      (upsamplers): ModuleList(\n",
      "        (0): Upsample2D(\n",
      "          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (3): UpBlock2D(\n",
      "      (resnets): ModuleList(\n",
      "        (0-1): 2 x ResnetBlock2D(\n",
      "          (norm1): GroupNorm(32, 512, eps=1e-05, affine=True)\n",
      "          (conv1): Conv2d(512, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "          (time_emb_proj): Linear(in_features=512, out_features=256, bias=True)\n",
      "          (norm2): GroupNorm(32, 256, eps=1e-05, affine=True)\n",
      "          (dropout): Dropout(p=0.0, inplace=False)\n",
      "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "          (nonlinearity): SiLU()\n",
      "          (conv_shortcut): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))\n",
      "        )\n",
      "        (2): ResnetBlock2D(\n",
      "          (norm1): GroupNorm(32, 384, eps=1e-05, affine=True)\n",
      "          (conv1): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "          (time_emb_proj): Linear(in_features=512, out_features=256, bias=True)\n",
      "          (norm2): GroupNorm(32, 256, eps=1e-05, affine=True)\n",
      "          (dropout): Dropout(p=0.0, inplace=False)\n",
      "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "          (nonlinearity): SiLU()\n",
      "          (conv_shortcut): Conv2d(384, 256, kernel_size=(1, 1), stride=(1, 1))\n",
      "        )\n",
      "      )\n",
      "      (upsamplers): ModuleList(\n",
      "        (0): Upsample2D(\n",
      "          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (4): UpBlock2D(\n",
      "      (resnets): ModuleList(\n",
      "        (0): ResnetBlock2D(\n",
      "          (norm1): GroupNorm(32, 384, eps=1e-05, affine=True)\n",
      "          (conv1): Conv2d(384, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "          (time_emb_proj): Linear(in_features=512, out_features=128, bias=True)\n",
      "          (norm2): GroupNorm(32, 128, eps=1e-05, affine=True)\n",
      "          (dropout): Dropout(p=0.0, inplace=False)\n",
      "          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "          (nonlinearity): SiLU()\n",
      "          (conv_shortcut): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1))\n",
      "        )\n",
      "        (1-2): 2 x ResnetBlock2D(\n",
      "          (norm1): GroupNorm(32, 256, eps=1e-05, affine=True)\n",
      "          (conv1): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "          (time_emb_proj): Linear(in_features=512, out_features=128, bias=True)\n",
      "          (norm2): GroupNorm(32, 128, eps=1e-05, affine=True)\n",
      "          (dropout): Dropout(p=0.0, inplace=False)\n",
      "          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "          (nonlinearity): SiLU()\n",
      "          (conv_shortcut): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))\n",
      "        )\n",
      "      )\n",
      "      (upsamplers): ModuleList(\n",
      "        (0): Upsample2D(\n",
      "          (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (5): UpBlock2D(\n",
      "      (resnets): ModuleList(\n",
      "        (0-2): 3 x ResnetBlock2D(\n",
      "          (norm1): GroupNorm(32, 256, eps=1e-05, affine=True)\n",
      "          (conv1): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "          (time_emb_proj): Linear(in_features=512, out_features=128, bias=True)\n",
      "          (norm2): GroupNorm(32, 128, eps=1e-05, affine=True)\n",
      "          (dropout): Dropout(p=0.0, inplace=False)\n",
      "          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "          (nonlinearity): SiLU()\n",
      "          (conv_shortcut): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (mid_block): UNetMidBlock2D(\n",
      "    (attentions): ModuleList(\n",
      "      (0): Attention(\n",
      "        (group_norm): GroupNorm(32, 512, eps=1e-05, affine=True)\n",
      "        (to_q): Linear(in_features=512, out_features=512, bias=True)\n",
      "        (to_k): Linear(in_features=512, out_features=512, bias=True)\n",
      "        (to_v): Linear(in_features=512, out_features=512, bias=True)\n",
      "        (to_out): ModuleList(\n",
      "          (0): Linear(in_features=512, out_features=512, bias=True)\n",
      "          (1): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (resnets): ModuleList(\n",
      "      (0-1): 2 x ResnetBlock2D(\n",
      "        (norm1): GroupNorm(32, 512, eps=1e-05, affine=True)\n",
      "        (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "        (time_emb_proj): Linear(in_features=512, out_features=512, bias=True)\n",
      "        (norm2): GroupNorm(32, 512, eps=1e-05, affine=True)\n",
      "        (dropout): Dropout(p=0.0, inplace=False)\n",
      "        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "        (nonlinearity): SiLU()\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (conv_norm_out): GroupNorm(32, 128, eps=1e-05, affine=True)\n",
      "  (conv_act): SiLU()\n",
      "  (conv_out): Conv2d(128, 5, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      ")\n"
     ]
    }
   ],
   "execution_count": 5
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# 模型测试与数据集加载",
   "id": "44bca20cb99e2dac"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-23T06:58:25.337463Z",
     "start_time": "2025-02-23T06:58:24.520619Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 测试输入输出\n",
    "# 选取采样通道\n",
    "sample_channel = 0\n",
    "sample_image = X_train_cwt_norm[0].unsqueeze(0)\n",
    "print(\"输入图像维度：\", sample_image.shape)\n",
    "\n",
    "print(\"Output shape:\", model(sample_image, timestep=0).sample.shape)"
   ],
   "id": "4e9bdfe403758ec6",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "输入图像维度： torch.Size([1, 5, 64, 256])\n",
      "Output shape: torch.Size([1, 5, 64, 256])\n"
     ]
    }
   ],
   "execution_count": 6
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-23T06:58:25.992187Z",
     "start_time": "2025-02-23T06:58:25.986134Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import torch\n",
    "from torch.utils.data import DataLoader, Dataset\n",
    "\n",
    "train_dataloader = DataLoader(X_train_cwt_norm, batch_size=config.train_batch_size, shuffle=True)"
   ],
   "id": "5d45ba6d839e8543",
   "outputs": [],
   "execution_count": 7
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "# 创建调度程序\n",
    "测试得到num_train_timesteps的合适值，查看加噪后的图像分布"
   ],
   "id": "dfaeeda49b2739c6"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-23T07:32:36.445316Z",
     "start_time": "2025-02-23T07:32:33.286157Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import torch\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "from diffusers import DDPMScheduler\n",
    "project_pic_dir = 'C:\\\\Users\\\\86136\\\\Desktop\\\\毕业设计\\\\picture'\n",
    "test_noise_steps = 100\n",
    "\n",
    "noise_scheduler = DDPMScheduler(num_train_timesteps=1000)\n",
    "noise = torch.randn(sample_image.shape)\n",
    "timesteps = torch.LongTensor([test_noise_steps])\n",
    "noisy_image = noise_scheduler.add_noise(sample_image, noise, timesteps)\n",
    "\n",
    "# 存储每一个下标时的图像正态分布相似度，用于绘制三维图像，x轴为时间步，y轴为均值，z轴为方差\n",
    "junzhi_list = []\n",
    "fangcha_list = []\n",
    "timestep_list = []\n",
    "\n",
    "\"\"\"for sample_pos in range(0, 1):\n",
    "    sample_image = X_train_cwt_norm[sample_pos].unsqueeze(0)\n",
    "    # 每加噪100步的图像，画出一张图，一共10张\n",
    "    fig, axs = plt.subplots(1, 10, figsize=(20, 2))\n",
    "    for i in range(0, 10):\n",
    "        timesteps = torch.LongTensor([test_noise_steps*i])\n",
    "        noisy_image = noise_scheduler.add_noise(sample_image, noise, timesteps)\n",
    "        axs[i].imshow(noisy_image[0, 0, :, :].detach().numpy(), aspect='auto')\n",
    "        axs[i].axis(\"off\")\n",
    "        timestep_list.append(i * 100)\n",
    "        junzhi_list.append(torch.mean(noisy_image).item())\n",
    "        fangcha_list.append(torch.var(noisy_image).item())\n",
    "        # 计算当前分布与正态噪声的相似度\n",
    "        axs[i].set_title(f\"Step {i * 100} \\n 均值：{torch.mean(noisy_image).item():.2f} \\n 方差：{torch.var(noisy_image).item():.2f}\")\n",
    "    plt.savefig(\"./tmpPic/sample_{}_noise_distribution.png\".format(sample_pos), dpi=300)\n",
    "    plt.show()\"\"\"\n",
    "\n",
    "for sample_pos in range(0, X_train_cwt_norm.shape[0]):\n",
    "    sample_image = X_train_cwt_norm[sample_pos].unsqueeze(0)\n",
    "    for i in range(0, 10):\n",
    "        timesteps = torch.LongTensor([test_noise_steps*i])\n",
    "        noisy_image = noise_scheduler.add_noise(sample_image, noise, timesteps)\n",
    "        timestep_list.append(i * 100)\n",
    "        junzhi_list.append(torch.mean(noisy_image).item())\n",
    "        fangcha_list.append(torch.var(noisy_image).item())\n",
    "    \n",
    "# 画出三维图像\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "ax.scatter(timestep_list, junzhi_list, fangcha_list)\n",
    "ax.set_xlabel('时间步')\n",
    "ax.set_ylabel('均值')\n",
    "ax.set_zlabel('方差')\n",
    "plt.savefig(\"./tmpPic/sample_noise_distribution_3d.png\", dpi=300, alpha=0.5)\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ],
   "id": "8e046d5ee7c6a73f",
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "print_png() got an unexpected keyword argument 'alpha'",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mTypeError\u001B[0m                                 Traceback (most recent call last)",
      "Cell \u001B[0;32mIn[46], line 52\u001B[0m\n\u001B[1;32m     50\u001B[0m ax\u001B[38;5;241m.\u001B[39mset_ylabel(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124m均值\u001B[39m\u001B[38;5;124m'\u001B[39m)\n\u001B[1;32m     51\u001B[0m ax\u001B[38;5;241m.\u001B[39mset_zlabel(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124m方差\u001B[39m\u001B[38;5;124m'\u001B[39m)\n\u001B[0;32m---> 52\u001B[0m \u001B[43mplt\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43msavefig\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43m./tmpPic/sample_noise_distribution_3d.png\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdpi\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m300\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43malpha\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m0.5\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[1;32m     53\u001B[0m plt\u001B[38;5;241m.\u001B[39mshow()\n",
      "File \u001B[0;32m~/miniconda3/lib/python3.8/site-packages/matplotlib/pyplot.py:1023\u001B[0m, in \u001B[0;36msavefig\u001B[0;34m(*args, **kwargs)\u001B[0m\n\u001B[1;32m   1020\u001B[0m \u001B[38;5;129m@_copy_docstring_and_deprecators\u001B[39m(Figure\u001B[38;5;241m.\u001B[39msavefig)\n\u001B[1;32m   1021\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21msavefig\u001B[39m(\u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs):\n\u001B[1;32m   1022\u001B[0m     fig \u001B[38;5;241m=\u001B[39m gcf()\n\u001B[0;32m-> 1023\u001B[0m     res \u001B[38;5;241m=\u001B[39m \u001B[43mfig\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43msavefig\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m   1024\u001B[0m     fig\u001B[38;5;241m.\u001B[39mcanvas\u001B[38;5;241m.\u001B[39mdraw_idle()  \u001B[38;5;66;03m# Need this if 'transparent=True', to reset colors.\u001B[39;00m\n\u001B[1;32m   1025\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m res\n",
      "File \u001B[0;32m~/miniconda3/lib/python3.8/site-packages/matplotlib/figure.py:3343\u001B[0m, in \u001B[0;36mFigure.savefig\u001B[0;34m(self, fname, transparent, **kwargs)\u001B[0m\n\u001B[1;32m   3339\u001B[0m     \u001B[38;5;28;01mfor\u001B[39;00m ax \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39maxes:\n\u001B[1;32m   3340\u001B[0m         stack\u001B[38;5;241m.\u001B[39menter_context(\n\u001B[1;32m   3341\u001B[0m             ax\u001B[38;5;241m.\u001B[39mpatch\u001B[38;5;241m.\u001B[39m_cm_set(facecolor\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mnone\u001B[39m\u001B[38;5;124m'\u001B[39m, edgecolor\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mnone\u001B[39m\u001B[38;5;124m'\u001B[39m))\n\u001B[0;32m-> 3343\u001B[0m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcanvas\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mprint_figure\u001B[49m\u001B[43m(\u001B[49m\u001B[43mfname\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[0;32m~/miniconda3/lib/python3.8/site-packages/matplotlib/backend_bases.py:2366\u001B[0m, in \u001B[0;36mFigureCanvasBase.print_figure\u001B[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001B[0m\n\u001B[1;32m   2362\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m   2363\u001B[0m     \u001B[38;5;66;03m# _get_renderer may change the figure dpi (as vector formats\u001B[39;00m\n\u001B[1;32m   2364\u001B[0m     \u001B[38;5;66;03m# force the figure dpi to 72), so we need to set it again here.\u001B[39;00m\n\u001B[1;32m   2365\u001B[0m     \u001B[38;5;28;01mwith\u001B[39;00m cbook\u001B[38;5;241m.\u001B[39m_setattr_cm(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mfigure, dpi\u001B[38;5;241m=\u001B[39mdpi):\n\u001B[0;32m-> 2366\u001B[0m         result \u001B[38;5;241m=\u001B[39m \u001B[43mprint_method\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m   2367\u001B[0m \u001B[43m            \u001B[49m\u001B[43mfilename\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m   2368\u001B[0m \u001B[43m            \u001B[49m\u001B[43mfacecolor\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mfacecolor\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m   2369\u001B[0m \u001B[43m            \u001B[49m\u001B[43medgecolor\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43medgecolor\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m   2370\u001B[0m \u001B[43m            \u001B[49m\u001B[43morientation\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43morientation\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m   2371\u001B[0m \u001B[43m            \u001B[49m\u001B[43mbbox_inches_restore\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m_bbox_inches_restore\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m   2372\u001B[0m \u001B[43m            \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m   2373\u001B[0m \u001B[38;5;28;01mfinally\u001B[39;00m:\n\u001B[1;32m   2374\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m bbox_inches \u001B[38;5;129;01mand\u001B[39;00m restore_bbox:\n",
      "File \u001B[0;32m~/miniconda3/lib/python3.8/site-packages/matplotlib/backend_bases.py:2232\u001B[0m, in \u001B[0;36mFigureCanvasBase._switch_canvas_and_return_print_method.<locals>.<lambda>\u001B[0;34m(*args, **kwargs)\u001B[0m\n\u001B[1;32m   2228\u001B[0m     optional_kws \u001B[38;5;241m=\u001B[39m {  \u001B[38;5;66;03m# Passed by print_figure for other renderers.\u001B[39;00m\n\u001B[1;32m   2229\u001B[0m         \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mdpi\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mfacecolor\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124medgecolor\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124morientation\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[1;32m   2230\u001B[0m         \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mbbox_inches_restore\u001B[39m\u001B[38;5;124m\"\u001B[39m}\n\u001B[1;32m   2231\u001B[0m     skip \u001B[38;5;241m=\u001B[39m optional_kws \u001B[38;5;241m-\u001B[39m {\u001B[38;5;241m*\u001B[39minspect\u001B[38;5;241m.\u001B[39msignature(meth)\u001B[38;5;241m.\u001B[39mparameters}\n\u001B[0;32m-> 2232\u001B[0m     print_method \u001B[38;5;241m=\u001B[39m functools\u001B[38;5;241m.\u001B[39mwraps(meth)(\u001B[38;5;28;01mlambda\u001B[39;00m \u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs: \u001B[43mmeth\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m   2233\u001B[0m \u001B[43m        \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43m{\u001B[49m\u001B[43mk\u001B[49m\u001B[43m:\u001B[49m\u001B[43m \u001B[49m\u001B[43mv\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mfor\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mk\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mv\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;129;43;01min\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mkwargs\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mitems\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mif\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mk\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;129;43;01mnot\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[38;5;129;43;01min\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mskip\u001B[49m\u001B[43m}\u001B[49m\u001B[43m)\u001B[49m)\n\u001B[1;32m   2234\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:  \u001B[38;5;66;03m# Let third-parties do as they see fit.\u001B[39;00m\n\u001B[1;32m   2235\u001B[0m     print_method \u001B[38;5;241m=\u001B[39m meth\n",
      "\u001B[0;31mTypeError\u001B[0m: print_png() got an unexpected keyword argument 'alpha'"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 46
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "# 训练模型\n",
    "创建优化器和学习率调度程序"
   ],
   "id": "e3d3ec6fbc39ce4a"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-23T08:37:11.443244Z",
     "start_time": "2025-01-23T08:37:11.436874Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from diffusers.optimization import get_cosine_schedule_with_warmup\n",
    "\n",
    "optimizer = torch.optim.AdamW(model.parameters(), lr=config.learning_rate)\n",
    "lr_scheduler = get_cosine_schedule_with_warmup(\n",
    "    optimizer=optimizer,\n",
    "    num_warmup_steps=config.lr_warmup_steps,\n",
    "    num_training_steps=(len(train_dataloader) * config.num_epochs),\n",
    ")"
   ],
   "id": "67346e9a6c333e07",
   "outputs": [],
   "execution_count": 17
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "构造评估模型的方法，使用 DDPMPipeline 生成一批样本图像并将其保存为网格：",
   "id": "79d6e2916b150f6d"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-23T08:37:11.466937Z",
     "start_time": "2025-01-23T08:37:11.444184Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from diffusers import DDPMPipeline\n",
    "from diffusers.utils import make_image_grid\n",
    "import os\n",
    "\n",
    "def evaluate(config, epoch, pipeline):\n",
    "    # 从随机噪声中生成一些图像（这是反向扩散过程）。\n",
    "    # 默认的管道输出类型是 `List[torch.Tensor]`\n",
    "    # 取sample_channel来展示\n",
    "    # print(\"生成图像......\")\n",
    "\n",
    "    images = pipeline(\n",
    "        batch_size=config.eval_batch_size,\n",
    "        generator=torch.Generator(device='cpu').manual_seed(config.seed),  # 使用单独的 torch 生成器来避免回绕主训练循环的随机状态\n",
    "        output_type=\"np.array\",\n",
    "    ).images\n",
    "\n",
    "    print(\"生成图像维度：\", images.shape)\n",
    "    # (batch, height, width, channel)\n",
    "\n",
    "    # 将生成的eval_batch_size个图像拼接成一张大图\n",
    "    pig, ax = plt.subplots(2, 10, figsize=(20, 4))\n",
    "    for i in range(2):\n",
    "        for j in range(10):\n",
    "            ax[i, j].imshow(images[i * 10 + j, :, :, sample_channel], aspect='auto')\n",
    "            ax[i, j].axis(\"off\")\n",
    "            ax[i, j].set_title(f\"Image {i * 10 + j}\")\n",
    "\n",
    "    plt.savefig(f\"{pic_dir}/{epoch:04d}.png\", dpi=400)\n",
    "    plt.close()"
   ],
   "id": "81c5b2ab6880fb58",
   "outputs": [],
   "execution_count": 18
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-23T08:37:11.482586Z",
     "start_time": "2025-01-23T08:37:11.468742Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from accelerate import Accelerator\n",
    "import torch.nn.functional as F\n",
    "from huggingface_hub import create_repo, upload_folder\n",
    "from tqdm.auto import tqdm\n",
    "from pathlib import Path\n",
    "import os\n",
    "\n",
    "def train_loop(config, model, noise_scheduler, optimizer, train_dataloader, lr_scheduler):\n",
    "    # Initialize accelerator and tensorboard logging\n",
    "    accelerator = Accelerator(\n",
    "        mixed_precision=config.mixed_precision,\n",
    "        gradient_accumulation_steps=config.gradient_accumulation_steps,\n",
    "        log_with=\"tensorboard\",\n",
    "        project_dir=os.path.join(config.output_dir, \"logs\"),\n",
    "    )\n",
    "    if accelerator.is_main_process:\n",
    "        if config.output_dir is not None:\n",
    "            os.makedirs(config.output_dir, exist_ok=True)\n",
    "        if config.push_to_hub:\n",
    "            repo_id = create_repo(\n",
    "                repo_id=config.hub_model_id or Path(config.output_dir).name, exist_ok=True\n",
    "            ).repo_id\n",
    "        accelerator.init_trackers(\"train_example\")\n",
    "\n",
    "    # Prepare everything\n",
    "    # There is no specific order to remember, you just need to unpack the\n",
    "    # objects in the same order you gave them to the prepare method.\n",
    "    model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n",
    "        model, optimizer, train_dataloader, lr_scheduler\n",
    "    )\n",
    "\n",
    "    global_step = 0\n",
    "\n",
    "    # Now you train the model\n",
    "    for epoch in range(config.num_epochs):\n",
    "        progress_bar = tqdm(total=len(train_dataloader), disable=not accelerator.is_local_main_process)\n",
    "        progress_bar.set_description(f\"Epoch {epoch}\")\n",
    "\n",
    "        for step, batch in enumerate(train_dataloader):\n",
    "            clean_images = batch\n",
    "            # Sample noise to add to the images\n",
    "            noise = torch.randn(clean_images.shape, device=clean_images.device)\n",
    "            bs = clean_images.shape[0]\n",
    "\n",
    "            # Sample a random timestep for each image\n",
    "            timesteps = torch.randint(\n",
    "                0, noise_scheduler.config.num_train_timesteps, (bs,), device=clean_images.device,\n",
    "                dtype=torch.int64\n",
    "            )\n",
    "\n",
    "            # Add noise to the clean images according to the noise magnitude at each timestep\n",
    "            # (this is the forward diffusion process)\n",
    "            noisy_images = noise_scheduler.add_noise(clean_images, noise, timesteps)\n",
    "\n",
    "            with accelerator.accumulate(model):\n",
    "                # Predict the noise residual\n",
    "                noise_pred = model(noisy_images, timesteps, return_dict=False)[0]\n",
    "                loss = F.mse_loss(noise_pred, noise)\n",
    "                accelerator.backward(loss)\n",
    "\n",
    "                if accelerator.sync_gradients:\n",
    "                    accelerator.clip_grad_norm_(model.parameters(), 1.0)\n",
    "                optimizer.step()\n",
    "                lr_scheduler.step()\n",
    "                optimizer.zero_grad()\n",
    "\n",
    "            progress_bar.update(1)\n",
    "            logs = {\"loss\": loss.detach().item(), \"lr\": lr_scheduler.get_last_lr()[0], \"step\": global_step}\n",
    "            progress_bar.set_postfix(**logs)\n",
    "            accelerator.log(logs, step=global_step)\n",
    "            global_step += 1\n",
    "\n",
    "        # After each epoch you optionally sample some demo images with evaluate() and save the model\n",
    "        if accelerator.is_main_process:\n",
    "            pipeline = DDPMPipeline(unet=accelerator.unwrap_model(model), scheduler=noise_scheduler)\n",
    "\n",
    "            if (epoch + 1) % config.save_image_epochs == 0 or epoch == config.num_epochs - 1:\n",
    "                evaluate(config, epoch, pipeline)\n",
    "\n",
    "            if (epoch + 1) % config.save_model_epochs == 0 or epoch == config.num_epochs - 1:\n",
    "                if config.push_to_hub:\n",
    "                    upload_folder(\n",
    "                        repo_id=repo_id,\n",
    "                        folder_path=config.output_dir,\n",
    "                        commit_message=f\"Epoch {epoch}\",\n",
    "                        ignore_patterns=[\"step_*\", \"epoch_*\"],\n",
    "                    )\n",
    "                else:\n",
    "                    pipeline.save_pretrained(config.output_dir)"
   ],
   "id": "b9119222c980b8fe",
   "outputs": [],
   "execution_count": 19
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# 启动训练",
   "id": "c94e6a53ad90e164"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-23T08:54:27.464600Z",
     "start_time": "2025-01-23T08:37:11.484096Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from accelerate import notebook_launcher\n",
    "\n",
    "args = (config, model, noise_scheduler, optimizer, train_dataloader, lr_scheduler)\n",
    "\n",
    "notebook_launcher(train_loop, args, num_processes=1)"
   ],
   "id": "4ad04817762c4e6a",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Launching training on one GPU.\n"
     ]
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